Sensor Instrumentation Research Articles

Real-Time Web-Based Monitoring System for Temperature, Humidity, and Solar Panels in Ramie Drying Facilities

Purpose: To address the manual monitoring challenges in processing ramie fibers, especially during drying. The purpose is to create a monitoring system that oversees room temperature, humidity, and the status of solar panels, crucial factors in ramie productivity.Methods: Real-time web-based system development that monitors room temperature, humidity, and the performance of solar panels in a ramie drying room using the Internet of Things architecture ESP32 with communication through GSM SIM 800L in rural areas.Results: The system can display real-time information such as temperature data, humidity, and electrical energy parameters derived from the solar panel's utilization in the ramie drying room. By doing so, users gain efficiency and effectiveness in obtaining information, significantly enhancing ramie fiber productivity.Novelty: Integration of sensor instruments, low-power ESP32 microcontrollers, GSM Telecommunication, Solar Cell Energy as a power source, and a real-time web-based Monitoring Information System implemented in a ramie drying dome. This simplifies the monitoring process and optimizes limited resources such as space, energy, telecommunications, and human resources, which are typically constrained infrastructure in the ramie fiber agricultural system.

Retrieving land surface reflectance anisotropy with Sentinel-3 observations and prior BRDF model constraints

Sentinel-3 supports governmental policies and European programmes such as the Copernicus Global Land Service with daily monitoring of the land surface by measuring the Earth's surface with its multi-band wide-swath visible and near-infrared radiometers. To fully exploit Sentinel-3 data sets, performing a correction of directional effects caused by the land surface reflectance anisotropy properties is mandatory. This correction requires first an estimation of the bidirectional reflectance distribution function (BRDF) to be properly achieved. The quality and robustness of global estimates of key land surface variables derived from space-borne sensors depend clearly on an accurate assessment of the spectral BRDF.Here, we present an algorithm aiming at harnessing the Sentinel-3's wide imaging swath to retrieve the land surface BRDF, approximating it via a kernel-driven semi-empirical model which can then be used to normalise the observations to a common Sun-sensor geometry. Our algorithm, named ReBeLS (Regularised BRDF inversion for Land Surface), uses a temporally regularised BRDF inversion approach and, together with prior knowledge of the land surface BRDF obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, assimilates Sentinel-3 surface reflectance observations. We focus on the results derived from observations provided by the Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensors; however, the algorithm can also be applied to reflectance observations acquired by the Sea and Land Surface Temperature Radiometer (SLSTR). The retrieved BRDF model parameters reproduce within uncertainties the directional signature as seen by the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor. The generated Sentinel-3 normalised surface reflectance products are a significant advance compared with time series of directional surface reflectance and maximum value composite images, showing a statistically significant (p≪0.05) correlation (r>0.9) with VIIRS nadir BRDF-adjusted surface reflectance. Furthermore, the ReBeLS Sentinel-3 BRDF products offer enough flexibility to allow users to compute spectral surface albedos or to normalise surface reflectance to any Sun-sensor configuration.

Integration of Autonomous Driving Functionalities for End-of-Line Production Transport Scenarios

Autonomous driving functionalities are increasingly being integrated into nowadays cars, which presents an opportunity for Automotive manufacturers to use these functionalities as part of the production process. This paper presents a case study of the integration of autonomous driving functionalities in a prototype vehicle for end-of-line production transport.The vehicle is equipped with sensor instrumentation, processing systems, and actuators to achieve automated driving, and the Robot Operating System (ROS) was utilized to achieve navigation on a pre-established route within a factory hall. The vehicle was also tested on a test track to emulate outdoor post-production testing stations. Complementary developed functions allow the vehicle to be effectively tracked and monitored inside and outside the demo factory.The use of autonomous driving functionalities in end-of-line production transport presents an opportunity for car manufacturers to streamline their production process. The successful integration of these functionalities in the vehicle using ROS and real-time tracking validates their use in a smart automotive factory. Further research and development in this area can lead to improved production efficiency and safety.

Spatiotemporal analysis of the link between VCDtrop NO2 and population size for provinces in Sumatra Island during 2012-2020

ABSTRACT Nitrogen dioxide is one of the primary air contaminants that can cause severe ecological and human health effects. This study used the Ozone Monitoring Instrument (OMI) sensor aboard AURA satellite to analyze the spatial and temporal variations of VCDtrop (Tropospheric vertical Columnar density) NO2 concentration in Sumatra, Indonesia, during 2012–2020. Based on the seasonal characteristics, the VCDtrop NO2 amounts in the east part of Sumatra were higher in the dry season and slightly lower in the wet season. The increasing trend was recorded in some provinces; North Sumatra, Riau, South Sumatra, and Lampung with the accumulation rate around 8–9 × 1015 molecules/cm2 from 2012 to 2014. Higher VCDtrop NO2 concentrations were associated with an increase in population size. As a whole, this finding can be useful to arrange air pollution management associated with population growth in urban areas.

(Invited) Therapeutic Drug Monitoring Using Quantitative Surface Enhanced Raman Scattering

We have for the past ten years developed nanopillar-based substrates for Surface Enhanced Raman Scattering (SERS) and have applied them in e.g. food quality monitoring and detection of toxins. In an ongoing project financed by the Danish Bio Innovation Institute, we are pursuing quantitative detection of small drug molecules in patient blood samples. The aim is to be able to detect e.g. concentration of anti-cancer medication within half an hour, hereby allowing rapid and personalized dose adjustment. I will show examples of recent findings and results within sensor development, instrumentation and blood analysis.

Life Detection and Microbial Biomarker Profiling with Signs of Life Detector-Life Detector Chip During a Mars Drilling Simulation Campaign in the Hyperarid Core of the Atacama Desert.

The low organic matter content in the hyperarid core of the Atacama Desert, together with abrupt temperature shifts and high ultraviolet radiation at its surface, makes this region one of the best terrestrial analogs of Mars and one of the best scenarios for testing instrumentation devoted to in situ planetary exploration. We have operated remotely and autonomously the SOLID-LDChip (Signs of Life Detector-Life Detector Chip), an antibody microarray-based sensor instrument, as part of a rover payload during the 2019 NASA Atacama Rover Astrobiology Drilling Studies (ARADS) Mars drilling simulation campaign. A robotic arm collected drilled cuttings down to 80 cm depth and loaded SOLID to process and assay them with LDChip for searching for molecular biomarkers. A remote science team received and analyzed telemetry data and LDChip results. The data revealed the presence of microbial markers from Proteobacteria, Acidobacteria, Bacteroidetes, Actinobacteria, Firmicutes, and Cyanobacteria to be relatively more abundant in the middle layer (40-50 cm). In addition, the detection of several proteins from nitrogen metabolism indicates a pivotal role in the system. These findings were corroborated and complemented on "returned samples" to the lab by a comprehensive analysis that included DNA sequencing, metaproteomics, and a metabolic reconstruction of the sampled area. Altogether, the results describe a relatively complex microbial community with members capable of nitrogen fixation and denitrification, sulfur oxidation and reduction, or triggering oxidative stress responses, among other traits. This remote operation demonstrated the high maturity of SOLID-LDChip as a powerful tool for remote in situ life detection for future missions in the Solar System.

Monitoring spatiotemporal changes of NO2 using TROPOMI and sentinel-5 images for Dhaka city and its surrounding areas of Bangladesh

Introduction: Discernable air pollution occurs in most developing countries due to rapid urbanization which can be parameterized by air, humidity, population density, temperature, contaminants, exorbitant fossil fuel consumption, and inadequate transportation. Nitrogen dioxide (NO2), one of the most widely recognized air pollutants, has a detrimental impact on human health explicitly or implicitly and considerably influences on atmospheric composition.
 Materials and methods: In this study, NO2 intensity was analyzed from 2018 with aiming to monitor spatiotemporal changes in Dhaka and its surrounding areas with the Tropospheric Monitoring Instrument (TROPOMI) sensor data. Copernicus Sentinel-5 Precursor satellite data was used in the Google Earth Engine platform to get the result.
 Results: The results revealed a strong relationship (R2 =0.9478) between the NO2 concentration and high population density and the temporal variation is higher during the pre-monsoon than throughout the post-monsoon. The reason behind is the lack of sunlight and the difficulty to break down the NO2, which causes the removal of NO2 from the atmosphere to proceed more slowly. In contrast, Land Use and Land Cover (LULC) are also impacted by the high concentration which is remains in the built-up area.
 Conclusion: This research mainly considered that how NO2 concentration measured from satellite images with temporal variation within a year and what factors strongly influence raising NO2 levels. This model can be used for policy-making to take proper initiatives to reduce NO2 concentrations. The result showed significant uses of TROPOMI with relating population density and LULC in Dhaka and its surrounding areas of Bangladesh.

Hydropedological digital mapping: machine learning applied to spectral VIS-IR and radiometric data dimensionality reduction

ABSTRACT Pedosphere-hydrosphere interface accounts for the association between soil hydrology and landscape, represented by topographic and Remote Sensing data support and integration. This study aimed to analyze different statistical radiometric and spectral data selection methods and dimensionality reduce environment-related data to support the classification of soil physical-hydric properties, such as soil basic infiltration rate (bir) and saturated hydraulic conductivity (Ksat); as well as to act in data mining processes applied to hydropedological properties digital mapping. Accordingly, research integrated information from Visible to Infrared (VIS-IR) spectral indices and Sentinel’s 2A mission Multispectral Instrument (MSI) sensor bands, terrain numerical modeling and aerogeophysics set to model soil-water content in two soil layers (0.00-0.20 m and 0.20-0.40 m). Pre-processed data were subjected to statistical analysis (multivariate and hypothesis tests); subsequently, the methods were applied (variation inflation factor - VIF, Stepwise Akaike information criterion – Stepwise AIC, and recursive feature elimination - RFE) to mine covariates used for Random Forest modeling. Based on the results, there were distinctions and singularities in spectral and radiometric data selection for each adopted method; the importance degree, and contribution of each one to soil physical-hydric properties have varied. According to the applied statistical metrics and decision-making criteria (highest R 2 and lowest RMSE / MAE), the chosen methods were RFE (0.00-0.20 m layers) and Stepwise AIC (0.20-0.40 m layers) - both concerned with the assessed variables (bir and Ksat). This approach captured the importance of environmental variables and highlighted their potential use in hydropedological digital mapping at Guapi-Macacu watershed.

Research on the effect of sand content on stress and durability of cement

With the progress of science and technology and the improvement of people’s quality of life, the demand for materials is getting higher and higher. As one of the most important civil engineering materials, concrete is widely used to repair a large number of broken infrastructures. Its production uses sand and aggregate from quarrying operation. If the use is unreasonable, a large amount of waste will be produced. In addition, there is a significant shortage of natural building raw materials in many regions (especially in developed regions). So improving the sustainability and durability of concrete materials can save transportation costs. This paper mainly discusses the influence of sand content in cement concrete on the performance of the material itself. The maximum pressure resistance of the corresponding specimen is measured by the pressure sensor and other experimental instruments, and compared with the specimen of different shape and volume under the condition of different sand rate. By changing the ambient temperature, the number of times that the specimen does not conform to the specified standard value is measured, and the durability of the specimen with different sand ratio is obtained. In this work, the optimal sand gravel ratio is successfully and relatively accurately measured. And it can be reasonably used in infrastructure construction.

Holistically Nested Edge Detection and particle filtering for subtle vibration extraction

Extracting and analyzing subtle motion via optical sensing techniques is becoming a more important research area within the vibration community. Computer vision approaches and machine learning architecture have been adopted to contest the complexity concerning hand instrumentation of sensors. Pre-treatment using a stochastic pattern for optical analysis or traditional wired sensing is not always permitted for large structures. In this work, Holistically Nested Edge Detection (HED) is utilized to highlight high-spatial-frequency content features in images for structural dynamic analysis. Following the accentuation of edge features, phase-based motion magnification (PMM) is used to amplify motion that is not visible to the naked eye. This permits the band-passing of higher order resonant frequencies for structural dynamic evaluation. Implementation of two-dimensional particle filtering (PF) is utilized to track the accentuated edge features in the presence of ghosting artifacts that appear post magnification. Within this work, compared to traditional edge-thresholding, the combination of HED and PF (HED-PF) is more proficient to handle noise and ultimately improves magnified image quality and modal parameter extraction. Uncertainties related to using PF for vibration measurement are analytically presented to provide guidance on the behavior of the algorithm. To demonstrate the effectiveness of HED-PF, experimental modal testing takes place on both a tier-structure and cantilever beam to demonstrate the approaches’ capability in handling varying sorts of excitation. Finally, introduction of background occlusions and non-ideal lighting scenarios are used to test the limitations of the approach. Ultimately, this will highlight HED-PF’s robust tracking capabilities compared to the current state of the art.

Numerical study of a D-shaped optical fiber SPR biosensor for monitoring refractive index variations in biological tissue via a thin layer of gold coated with titanium dioxide

This study aims to explore the numerical analysis of the impact of integrating titanium oxide (TiO2) into a D-shaped optical fiber biosensor based on surface plasmon resonance (SPR). A thin layer of gold (Au) is applied to the flat section of the fiber, which is also coated with a thin layer of titanium dioxide (TiO2). The behavior and performance of the proposed biosensor for use in biological environments are evaluated using the finite element method (FEM). The optical response of SPR-based biosensors is highly dependent on the analyzed medium, enabling the detection of pathogenic cells and abnormalities in biological tissues. This provides high sensitivity and selectivity, as well as real-time detection accuracy and speed. In this study, the biosensor is incorporated into a biological medium with a refractive index that varies with wavelength. A series of simulations have been conducted to plot the spectra of transmissions, absorptions, and dielectric losses obtained in the output of the sensor instrument. From these spectra, the corresponding surface plasmon resonance (SPR) wavelength (λSPR) within the visible-near-infrared band can be determined. Taking into account the various parameters that influence plasmonic interactions, the biosensor's performance parameters, in particular sensitivity and refractive index resolution have been optimized. Our results show that the presence of the TiO2 layer improves the performance of the proposed sensor and offers the possibility of adjusting the resonance wavelength (λSPR). In addition, our proposed sensor can achieve a better resolution of 7.50×10-6[RIU] in 1.34-143 range of analyte refractive index, which notably exceeds that of current technologies. This opens up new prospects in the field of chemical and biological detection.

A wide-range UAC sensor for the classification of hyperuricemia in spot samples

Hyperuricemia (HUA) has received wide attention as an independent risk factor for various chronic diseases. HUA is usually asymptomatic, and the related damage can be reduced by effective classification and treatment according to uric acid clearance (UAC). UAC is a calculated ratio based on the uric acid level in blood and urine. This important method is not universally used due to the inconvenience of collecting 24-h urine samples in the clinic, and most sensors are limited by the need for wide ranges and for two testing samples. In this study, a pH-sensitive urate oxidase-modified electrochemical sensor with filter membrane was proposed to calculate UAC by detecting uric acid in blood and urine. The results demonstrated that the sensor had high selectivity for uric acid with a detection limit of 0.25 μM in 5 μL spot sample, the wide linear range was 2.5–7000 μM, and the impact of the sample pH was calibrated. The linear correlation of the measurement results between the UAC sensor and clinical instrument was higher than 0.980 for 87 patients. The change in UAC in spot urine may reflect alteration in body-transport mechanisms. Thus, the UAC sensor may open a new window for the management of HUA and broaden its application in point-of-care testing.

Piezoelectric Wafer Active Sensor Transducers for Acoustic Emission Applications.

Piezoelectric materials are defined by their ability to display a charge across their surface in response to mechanical strain, making them great for use in sensing applications. Such applications include pressure sensors, medical devices, energy harvesting and structural health monitoring (SHM). SHM describes the process of using a systematic approach to identify damage in engineering infrastructure. A method of SHM that uses piezoelectric wafers connected directly to the structure has become increasingly popular. An investigation of a novel pitch-catch method of determining instrumentation quality of piezoelectric wafer active sensors (PWASs) used in SHM was conducted as well as an investigation into the effects of defects in piezoelectric sensors and sensor bonding on the sensor response. This pitch-catch method was able to verify defect-less instrumentation quality of pristinely bonded PWASs. Additionally, the pitch-catch method was compared with the electromechanical impedance method in determining defects in piezoelectric sensor instrumentation. Using the pitch-catch method, it was found that defective instrumentation resulted in decreasing amplitude of received and transmitted signals as well as changes in the frequency spectrums of the signals, such as the elimination of high frequency peaks in those with defects in the bonding layer and an increased amplitude of around 600 kHz for a broken PWAS. The electromechanical impedance method concluded that bonding layer defects increase the primary frequency peak's amplitude and cause a downward frequency shift in both the primary and secondary frequency peaks in the impedance spectrum, while a broken sensor has the primary peak amplitude reduced while shifting upward and nearly eliminating the secondary peak.

Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision Making—Part 1: Framework Development

Data management systems are increasingly used in industrial processes. However, data collected as part of industrial process operations, such as sensor or measurement instruments data, contain various sources of errors that can hamper process analysis and decision making. The authors propose an operating-regime-based data processing framework for industrial process decision making. The framework was designed to increase the quality and take advantage of available process data use to make informed offline strategic business operation decisions, i.e., environmental, cost and energy analysis, optimization, fault detection, debottlenecking, etc. The approach was synthesized from best practices derived from the available framework and improved upon its predecessor by putting forward the combination of process expertise and data-driven approaches. This systematic and structured approach includes the following stages: (1) scope of the analysis, (2) signal processing, (3) steady-state operating periods detection, (4) data reconciliation and (5) operating regime detection and identification. The proposed framework is applied to the brownstock washing department of a dissolving pulp mill. Over a 5-month period, the process was found to be in steady-state 32% of the time. Twenty (20) distinct operating regimes were identified. Further processing with the help of data reconciliation techniques, principal component analysis and k-means clustering showed that the main drivers explaining the operating regimes are the pulp level in tanks, its density, and the shower wash water flow rate. Additionally, it was concluded that the top four persistently problematic sensors across the steady-state spans that would need to be verified are three flow meters (06FIC137, 06FIC152, and 06FIC433), and one consistency sensor (06NIC423). This information was relayed to process experts contacts at the plant for further investigation.

Improving sea surface floating matter identification from Sentinel-2 MSI imagery using optical radiative simulation of neighborhood difference.

The reflectance difference (ΔR) between a floating matter pixel and a nearby water reference pixel is a method of atmospheric radiation unmixing. This technique unveils target signals by referencing the background within the horizontal neighborhood. ΔR is effective for removing the mixed-pixel effect and partial atmospheric path radiance. However, other atmospheric interference sources in the difference pixel, including atmospheric extinction and sunglint, need to be clarified. To address these challenges, we combined in situ floating matter endmember spectra for simulation and Sentinel-2 Multispectral Instrument (MSI) sensors for validation. We focused on radiative transfer simulation of horizontal neighborhood and vertical atmospheric column, investigating the bilateral conversion of ΔR between bottom-of-atmosphere (BOA) and top-of-atmosphere (TOA) signals, and clarifying how the atmosphere affects the difference pixel (ΔR) and floating matter identification. Results showed that direct use of TOA ΔR works in discriminating algae from non-algae floating matters under weak sunglint, and is a suitable candidate for no bother with atmospheric correction, least uncertain, and wider coverage. And then, sunglint interference is also inevitable, whether serious or not.

Force–Torque Sensors for Minimally Invasive Surgery Robotic Tools: An Overview

Robot-assisted surgery is a multidisciplinary research field, which includes many subjects (e.g., medicine and surgery, engineering, mechanics, electronics, computer science, physics, chemical, material science, robotics and many others). Sensors for physical interaction, during robot-assisted surgery, integrate some of these disciplines and are an open challenge. In this survey, we analyse some of the main design solutions of force-torque sensors for surgical instruments with a critical approach, including design and application constraints: robotic surgery environment, surgeon perception, general design architectures of force-torque sensors and force-torque sensors used in robot-assisted minimally invasive surgery. The crucial aspects are analysed in detail, defining technological limitations and future perspectives of sensing instruments in surgical applications.

Nuclear Magnetic Resonance for Targeted Metabolomics and Biochemical Sensor

NMR spectroscopy is quantitative, highly reproducible, non-selective and non-destructive. However, NMR costs and complexity hinder a use as point-of-care and biochemical sensor instrumentation. Low field NMR (LF-NMR) is an inexpensive and low footprint technique to obtain physical, chemical, electronic and structural information on small molecules, but suffers from poor spectral dispersion, especially when applied to the analysis of mixtures. Subspectral editing employing optimal control pulses is a suitable approach to cope with the severe signal superpositions in complex mixture spectra at low field.
 We have calculated the optimal control pulse shapes at 0.5 T NMR frequency using the Krotov algorithm. Downsizing the complexity of the algorithm from exponential to polynomial is shown to be possible by using a system approach with each system corresponding to a (small) molecule. In this way compound selective excitation pulses can be calculated. The signals of substructures of the cyclopentenone molecule were excited using optimal control pulses calculated by the Krotov algorithm demonstrating the feasibility of subspectral editing. Likewise, for a mixture of benzoic acid and alanine, editing of the signals of either benzoic acid or alanine employing optimal control pulses was shown to be possible.
 The obtained results are promising and can be extended to the targeted analysis of complex mixtures such as biofluids or metabolic samples at low field strengths opening access for benchtop NMR to point of care settings. Moreover, the LF-NMR was improved to allow a unique detection and reliable quantification of metabolites in biofluids like blood, urine, cerebrospinal fluid, or even tissue. In contrast to standard large scale NMR equipment the portable LF-NMR allows for an analysis of the chemical composition of biofluid samples directly at the patient (point-of-care).
 LF-NMR can be freely programmable for detecting and detecting and sensing sets of small molecules which deliver information on the health status of a person. Such an analytical tool would be useful for the prediction, diagnosis, monitoring and prognosis of diseases, and for supervision of therapeutic interventions.

Kinetic Features of Alpha Particles in a Pestchek-like Magnetic Reconnection Event in the Solar Wind Observed by Solar Orbiter

The acceleration and heating of solar wind particles by magnetic reconnection are important mechanisms in space physics. Although alpha particles (4He2+) are the second most abundant population of solar wind ions, their kinetic behavior in solar wind magnetic reconnection is not well understood. Using the high-energy (1500–3000 eV) range of the Solar Wind Analyser/Proton–Alpha Sensor instrument on board Solar Orbiter, we study the kinetic features of alpha particles in an exhaust region of a Pestchek-like solar-wind reconnection event with a weak guide field. A pair of back-to-back compound discontinuities is observed in the exhaust region. We find that the plasma in the magnetic exhaust region is heated and bounded by slow shocks (SSs), while the accelerated reconnection jet is bounded by rotational discontinuities (RDs). The SSs are outside the RDs, which is not expected from the magnetohydrodynamical prediction. We suggest this different location of the discontinuities is due to the enhanced parallel temperature T p∥ > T p⊥, which reduces the local Alfvén speed in the exhaust region, allowing the SSs to propagate faster than the RDs. Inside the exhaust region, the guide field is dominant. We find a two-population distribution of the alpha particles. These two populations are field aligned downstream the SSs and shift to have a perpendicular offset in the reconnection jet, suggesting that the change of the magnetic field at the RDs has similar timescales with the proton gyroperiod, but faster than those of the alpha particles, such that the alpha particles behave like pickup ions.

Introduction to EarthCARE synthetic data using a global storm-resolving simulation

Abstract. Pre-launch simulated satellite data are useful to develop retrieval algorithms and to facilitate the rapid release of retrieval products after launch. Here we introduce the Japanese Aerospace Exploration Agency's (JAXA) EarthCARE synthetic data based on simulations using a 3.5 km horizontal-mesh global storm-resolving model. Global aerosol transport simulation results are added for aerosol retrieval developers. Synthetic data were produced corresponding to the four EarthCARE instrument sensors, namely a 94 GHz cloud-profiling radar (CPR), a 355 nm atmospheric lidar (ATLID), a seven-channel multispectral imager (MSI), and a broadband radiometer (BBR). JAXA EarthCARE synthetic data include a standard product with data for two orbits and a research product with shorter frames and more detailed instrument settings. In the research products, random errors in the CPR are considered based on the observation window, and noise in ATLID signals are added using a noise simulator. We consider the spectral misalignment effect of the visible and near-infrared MSI channels based on response functions depending on the angle from the nadir. We introduce plans for updating the JAXA EarthCARE synthetic data using large eddy simulation model data and the implementation of a three-dimensional radiation model. The JAXA EarthCARE synthetic data are available publicly.

Infrared Photon-Number-Resolving Imager Using a Skipper Charge-Coupled Device

Imaging in a broad light-intensity regime with a high signal-to-noise ratio is a key capability in fields as diverse as quantum metrology and astronomy. Achieving high signal-to-noise ratios in quantum imaging leads to surpassing the classical limit in parameter estimation. In astronomical detection, the search for habitable exoplanets demands imaging of atmospheres in the infrared to look for biosignatures. These optical applications are hampered by detection noise, which critically limits their potential, and thus demands photon-number and spatial-resolution detectors. Here we report an imaging device in the infrared wavelength range able to arbitrarily reduce the readout noise. We built a measured exposure skipper-CCD sensor instrument equipped with a thick back-illuminated sensor, with photon-number-resolving capability in a wide dynamic range, spatial resolution, high quantum efficiency in the near-infrared and ultralow dark counts. This device allows us to image objects in a broad range of intensities within the same frame and, by reducing the readout noise to less than 0.2e${}^{\ensuremath{-}}$, to distinguish even those shapes with less than two photons per pixel, unveiling what was previously hidden in the noise. These results pave the way for building high-standard infrared imagers based on skipper CCDs.